The present invention is motivated by the application of virtual metrology (VM) in semiconductor manufacturing where the goal is to predict wafer quality for purposes of controlling and monitoring the processing of wafers.
Since the manufacturing process is very complex, after each step, semi-finished wafers in selected lots are sampled for actual metrology to monitor the process performance. Based on the actual metrology sampling results, the process control system will take appropriate action to adjust the process variables by lot or by wafer. In addition, if the actual metrology sampling results are inconsistent with given quality standards, the defective wafers are not sent to the following recipe processes in order to reduce production cost.
Modern semiconductor processing tools publish large amounts of real-time data which can reflect the actual processing conditions, such as temperature, pressure, gas flow, and throttle valve positions, to name a few. The large amounts of data present an opportunity to predict or classify wafer quality (process output) based on process variables. The model-based prediction of a process outcome, which is used instead of actual physical measurements of that outcome, or in addition to the actual measurements, is referred to as VM in the semiconductor industry. In contrast to lot-by-lot process control, the predicted process output in the absence of actual metrology provides additional and real-time information for run-to-run process control at the wafer level (i.e., wafer-by-wafer).
U.S. Pat. No. 7,778,715 discloses a method for obtaining a state description associated with a system having a component and automatically obtaining a substantially optimal parameterization for the component based on one or more operant characteristics of the component predicted by a behavior prediction model using combinations of the system's state description and a set of possible parameterizations for the component. U.S. Patent Application Publication No. 2011/0202160 discloses methods, apparatuses, and systems for determining adaptive predictive algorithms for VM. The computer implemented method taught by the prior art identifies a plurality of predictive algorithms and determines when to use one or more of the plurality of predictive algorithms to predict one or more VM variables in a manufacturing facility.
In the prior art, as disclosed in U.S. Pat. No. 7,546,170, the trend of a predictive term is estimated based on past values and used as a controller. U.S. Pat. No. 6,249,712 discloses a system for adaptively controlling a wide variety of complex processes, despite changes in process parameters and despite both sudden and systematic drifts in the process. The prior art system estimates the dynamic component of a drifting process or system and thereby identifies the trend of output response variables of the controlled process. Using this information, the system taught in the prior art predicts future outputs based on a history of past and present inputs and outputs, thereby recommending the necessary control action or recipe to cancel out the drifting trend.
U.S. Patent Application Publication No. 2009/0276075 discloses, in a complex manufacturing environment for producing semiconductor devices, a predicted quality distribution in the form of a graded die forecast may be monitored with respect to changes in order to more efficiently identify factory disturbances. The prior art teaches the selection of process variables to build a top-level mathematical model for subsequent processes as disclosed by U.S. Pat. Nos. 7,996,102. U.S. Pat. No. 7,533,313 discloses a method for converting data that includes generating a first data vector of data measurements related to processing of at least one workpiece.
The prior art disclosed in U.S. Patent Application Publication No. 2011/0320026 presents methods for processing the raw wafer manufacturing data to select the best data therefrom in accordance with at least one of a plurality of knowledge-, statistic-, and effect-based processes and tracking features for generating prediction and control data therefrom. U.S. Patent Application Publication No. 2006/0129257, U.S. Pat. No. 7,343,217, and U.S. Patent Application Publication No. 2010/0312374 disclose a semiconductor manufacturing information framework including VM to operate a processing tool for semiconductor manufacturing.
The performance of prior art VM applications can be dramatically impacted by the lack of training examples, which is the direct result of hundreds of process variables with intricate dependencies, hidden patterns, tool wear, and process dynamics.
In addition, a given process in semiconductor manufacturing is usually running on multiple fabrication tools and each of which has multiple chambers with multiple sides that possess different capabilities or are controlled independently. For VM applications, each chamber-side of each of the fabrication tools can be modeled to predict the quality of wafers produced therein. In current practice due to issues of cost and the frequency of actual metrology sampling, the building of one unique model at a time that predicts the quality of products produced in a single side of a single tool requires a great deal of time and expense in order to collect sufficient training examples. Due to tool wear, process dynamics and other reasons, unseen data patterns occur often and the performance of the prediction model can degrade quickly. Under current practices, it is difficult to maintain and update each of the unique models that predict wafer quality in a single side of a single fabrication tool for the hundreds of processes that each of the wafers must go through during manufacturing. Accordingly, there is a need to address the problems in the prior art.